| Field pe（aPisum sativum L.）and faba bean (Vicia faba L.) are important cold-season grain legumescultivated in China. Cold-season grain legumes are popularly nutritional sources of rich protein,carbohydrates and fiber, as well as many total polyphenol, essential vitamins and minerals but low oiland sodium. Both legumes are grown at altitude from hundreds meters to three or four thousands metersfar from city pollution and low fertilizer needed, in which ecological environment are clean and healthy.In global trade, field pea and faba bean are always considered as high-value products due to nutrientvalue and excellent ecological environment, as well as expensive labor cost. Consumers have becomemore health conscious, demanding and willing to pay for the “good quality”. Quality traits of the foodlegumes are essential identify index for the choice of germplasm resource, food procedure and evenprocessing equipments designed.This research analyzed the content of protein, starch, oil and total polyphenol in field pea (256varieties) and faba bean (244varieties) by chemical methods. The coefficient of correlation (r2) betweenquality traits was calculated. The legumes germplasm with excellent quality traits were screened and atotal of96pea accessions and102faba bean accessions were obtained, with the ratio of37.5%and41.8%, respectively.By visual-observe method, the relation between apparent traits (seed shape, seed coat color andhilum color) and quality traits of pea accessions were researched. The difference of quality traits of peaaccessions in certain sorts of seed shapes, seed coat color and hilum color was significant (P<0.05).Also faba bean did in certain sorts of seed length, seed coat colors and hilum colors be significant (P<0.05). Range of accuracy of prediction by combined apparent traits was from57.1%to100%withlower value of coefficient of variation.Feasibility of the Fourier Transform Near-Infrared Spectroscopy (FT-NIRS) on estimating qualitytraits in pea and faba bean were evaluated in current study, respectively. Firstly, using pea powder assample, the influence of scanning conditions, such as different resolution, different scan times andsample granularity, were identified to unify the scanning term to get high quality spectra. Spectra wereobtained by Matrix-I FT-NIR spectrometer (Bruker Optics, Ettlingen, German) at25℃with repeated9times at different levels of resolutions, scan times and particle size and performed by OPUS6.5foraverage spectrum. By the difference of value of absorption bands and SD patterns of the average spectra,optimal scanning conditions were resolution of16cm-1, scanning of64times, and sample particle sizeof60mush.Secondly, estimation models were developed for protein, starch, oil, and total polyphenol of peaand faba bean using near infrared spectroscopy (NIRS), respectively. A total of190pea samples weremeasured in both milled powder and intact seed forms. Partial least squares (PLS) regression wasapplied for model development. The optimal models were powder-based for protein and starch withresidual predictive deviation (RPD) of5.88and5.82as well as coefficients of correlation (r2) of0.99 and0.99, respectively. The optimal models were seed-based for protein, starch, oil and total polyphenolwith coefficients of correlation (r2) of0.97,0.95,0.94, and0.94, respectively. High values of correlationcoefficient (r2) revealed that models had good predictive capacities for rapid germplasm analysis of pea.A total of244faba bean samples were also measured in both milled powder and intact seed forms.Models of powder were generally superior to models in intact seed. The optimal seed powder-basedmodels for protein, starch, oil and total polyphenol had coefficients of correlation (r2) of0.97,0.93,0.81,and0.89, respectively. The optimal models of faba bean were seed-based for protein, starch, oil andtotal polyphenol with coefficients of correlation (r2) of0.88,0.89,0.81, and0.84, respectively. Thirdly,the influence of temperature and water content of samples for prediction of pea seed models of proteinand starch were measured. The results showed robust prediction were obtained when temperature andwater content of samples were close to the condition of samples in calibration set. Temperature regionwas above15℃and best at25℃. The content of water was below11.2%near to calibration samplesof ambient temperature drying.To explore the relationship between quality traits and producing regions,150pea varieties withspecific information were analyzed by two-step cluster analysis. Three distinct groupings were obtainedwith obvious features. Group1was in low protein content at production area of North and Central China.Group2was in low starch content at production area of West China. Group3was in high protein, starchand oil content at production area of Southwest China. The clustering accuracy was62.5%. Therelationship between nutrient contents and producing areas of faba bean accessions were determined bytwo-step cluster analysis. Three distinct groupings of faba bean were obtained with region-constituentfeatures, i.e., Group1of high oil at production area of Southwest China, Group2of high protein atproduction area of North and Central China, and Group3of high starch as well as total polyphenol atproduction area of West China. The clustering accuracy was79.5%. The value of clustering accuracymight be related with influence of cultivating location on survive of the crops. Moreover, the nutritioncontents were affected by seeding date, longitude, latitude, and altitude of plant location. Clusteranalysis revealed that the contents of quality traits in both legumes were strongly influenced bygeographical factors. |
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